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Problem solution · C++

Maximum Total Sum of K Selected Elements

Maximum Total Sum of K Selected Elements: a C++ solution using heap or priority queue. Learn the idea, check the complexity, and read the full code, with credit to Kamyu LeetCode Solutions.

Technique
Heap or priority queue
Source
Kamyu LeetCode Solutions
Length
53 lines
Start with the idea.

Try the problem first. If you get stuck, read the approach below, then write your own solution. The full code is at the bottom.

Approach

Heap or priority queue

For Maximum Total Sum of K Selected Elements, the implementation repeatedly takes the currently best candidate from a heap while inserting newly available choices.

  1. Define the priority key and whether the smallest or largest item should lead.
  2. Push each candidate when it becomes eligible.
  3. Discard stale entries when necessary and process the best live candidate.

Code notes

  • 53 lines of C++ from the credited upstream file maximum-total-sum-of-k-selected-elements.cpp.
  • The implementation visibly relies on sequence storage, work queue.
  • 4 loop blocks detected.

Complexity

Count heap pushes and pops; each normally contributes a logarithmic factor in the heap size.

Check the problem constraints before deciding whether this complexity will pass.

Source

Code and credit

This code comes from Kamyu LeetCode Solutions by kamyu104 and is used under the MIT licence.

Open problem ↗Original source ↗
Full codeMaximum Total Sum of K Selected Elements · C++C++
Use this to learn the idea, then write your own version.
// Time:  O(nlogk)// Space: O(k) // heap, greedyclass Solution {public:    long long maxSum(vector<int>& nums, int k, int mul) {        priority_queue<int, vector<int>, greater<int>> min_heap;        for (const auto& x : nums) {            min_heap.emplace(x);            if (size(min_heap) == k + 1) {                min_heap.pop();            }        }        int64_t result = 0;        while (!empty(min_heap)) {            const auto x = min_heap.top(); min_heap.pop();            result += x * static_cast<int64_t>(max(mul - static_cast<int>(size(min_heap)), 1));        }        return result;    }}; // Time:  O(nlogk)// Space: O(k)// sort, greedyclass Solution2 {public:    long long maxSum(vector<int>& nums, int k, int mul) {        partial_sort(begin(nums), begin(nums) + k, end(nums), greater<int>());        int64_t result = 0;        for (int i = 0; i < k; ++i) {            result += nums[i] * static_cast<int64_t>(max(mul - i, 1));        }        return result;    }}; // Time:  O(nlogn)// Space: O(1)// sort, greedyclass Solution3 {public:    long long maxSum(vector<int>& nums, int k, int mul) {        sort(begin(nums), end(nums), greater<int>());        int64_t result = 0;        for (int i = 0; i < k; ++i) {            result += nums[i] * static_cast<int64_t>(max(mul - i, 1));        }        return result;    }};

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